How to Start a Data Project When You Have No Idea What to Analyze
A beginner’s guide to finding ideas, asking questions, and just getting started
If you're learning data science, you’ve probably heard this advice a hundred times:
“Build your own projects to learn faster and stand out.”
But what if your mind goes completely blank when it’s time to start?
You open your notebook, crack your knuckles, and then stare at the screen thinking,
“What should I even analyze?”
It’s one of the most frustrating parts of being a beginner. The desire is there—but the ideas aren’t. If that sounds like you, don’t worry. You’re not alone, and you’re not doing anything wrong.
Let’s talk about how to get unstuck and start your own data project—even when you feel like you have zero inspiration.
Step 1: Don’t Wait for a “Big Idea”
Here’s the truth: your first data project doesn’t need to be groundbreaking.
You’re not writing a thesis or solving world hunger. You’re just practicing.
Start with something small, familiar, and maybe even a little boring.
Examples:
Your Spotify listening history
Your screen time data
The last 100 books you’ve read
NBA player stats
Coffee prices over time
Why do these work? Because:
The data is accessible
You already understand the context
You can come up with questions naturally
You don’t need a “brilliant” idea—you just need any data that sparks curiosity.
Step 2: Pick a Dataset That Feels Personal or Fun
If you’re struggling to find something to analyze, start with your own life. Personal data is powerful because you don’t have to spend hours trying to “understand” it—it’s already part of your world.
Here are a few ideas to get you going:
Spotify – Analyze your top artists, genres, or how your music taste changes over months
Google Takeout – See your location history, search patterns, or YouTube watch time
Netflix Viewing History – Are you more into thrillers in winter? Who knows!
Apple Screen Time – Visualize your phone usage and app habits
Budget Tracker or Bank Statements – Make charts of spending habits (be safe with personal data!)
Working with something personal makes the project instantly more interesting and meaningful.
Step 3: Browse Popular Datasets
If you want something more “public,” check out these great places to find beginner-friendly datasets:
Kaggle Datasets – Search by theme (sports, business, health, etc.)
data.world – A huge variety of public data sources
UCI Machine Learning Repository – Classic datasets for practicing
FiveThirtyEight GitHub – Well-documented, storytelling-style data
Google Dataset Search – Like Google, but for datasets
Browse around. If a title catches your eye—click it. Don’t overthink.
Step 4: Ask Simple Questions First
Many beginners get stuck thinking they need to build a complex machine learning model. You don’t.
Start by asking basic questions like:
What’s the average value?
What’s the most common category?
How has this changed over time?
Are there any weird outliers?
What trends or patterns do I notice?
For example, if you’re analyzing your Spotify data:
Who were my top artists this year?
Did I listen to more music on weekends?
How often do I skip songs?
These questions may seem small—but that’s where exploration begins.
Step 5: Use the “Three-Stage” Project Template
If you’re still unsure how to structure your project, here’s a simple formula that works every time:
1. Exploration
Load the data, clean it, check for missing values, and get a feel for what you're working with.
2. Visualization
Make a few charts: bar plots, scatter plots, line graphs—whatever helps you see the story in the data.
3. Insights
Write down 3–5 key takeaways. What surprised you? What patterns did you find? What questions do you now want to ask?
That’s it. That’s a full data project.
Step 6: Don’t Worry About the Toolset Too Much
Whether you’re using Python, R, Excel, or Google Sheets, it doesn’t matter. The point is to learn how to think with data.
If you’re just starting out, Python with pandas and matplotlib is a great combo. But if you’re more comfortable in Excel, start there. You can always “translate” your project into code later.
The most important part is learning how to ask good questions and communicate what you find.
Step 7: Make It Shareable (Even If It’s Small)
Once you finish your project, don’t let it sit in silence. Publish it somewhere:
A blog post (Medium is great)
A GitHub repo with a README
A LinkedIn post with a few screenshots and takeaways
A short YouTube video walkthrough
Even if it feels basic, share it. You’ll get better feedback, improve your communication, and maybe even inspire someone else who’s stuck like you were.
And remember: the goal isn’t perfection—it’s progress.
Bonus Tips for Idea Generation
Still stuck? Here are 10 quick prompts to kickstart your brain:
What’s something you already track (sleep, food, workouts)?
What are you curious about in the news?
Have you seen a viral tweet with data? Try recreating it.
Pick your favorite sport—analyze player stats or team trends.
Compare movie ratings from IMDb vs Rotten Tomatoes.
Visualize COVID trends in your hometown.
Analyze public transportation usage in your city.
Check if weather patterns affect your mood (or steps).
Review public funding or budgets from a local government.
Analyze TED Talks by topic, view count, or duration.
The possibilities are endless. The secret is: pick one and start.
If you’ve been staring at a blank screen thinking, “I don’t know what to analyze,” now you do.
You don’t need a fancy dataset, a complex model, or a perfect plan. You just need to get curious, pick something—anything—and start exploring.
Once you begin, ideas will flow more easily. Your confidence will grow. And soon enough, you’ll look back and laugh at how hard starting felt.
Just take that first step.